Learn QUANTLIB with Real Code Examples

Use analytic pricing when possible

Limit Monte Carlo paths for initial testing

Cache term structures and volatilities

Vectorize computations in Python for speed

Profile code for bottlenecks in C++

Validate external market data sources

Avoid floating-point rounding errors in critical calculations

Test custom models extensively

Maintain version control for pricing engines

Document assumptions and approximations

Log simulation outputs

Track pricing errors or discrepancies

Monitor Monte Carlo convergence

Check term structure consistency

Audit calibration and scenario analysis

Follow C++ and Python best practices

Document instruments and engines

Unit test each pricing engine

Validate numerical accuracy

Maintain version control